U.S. patent number 8,644,402 [Application Number 12/950,452] was granted by the patent office on 2014-02-04 for apparatus and method for compressive sensing tap identification for channel estimation.
This patent grant is currently assigned to QUALCOMM, Incorporated. The grantee listed for this patent is Farrokh Abrishamkar, Ori Shental, Ni-Chun Wang, Yingqun Yu. Invention is credited to Farrokh Abrishamkar, Ori Shental, Ni-Chun Wang, Yingqun Yu.
United States Patent |
8,644,402 |
Abrishamkar , et
al. |
February 4, 2014 |
Apparatus and method for compressive sensing tap identification for
channel estimation
Abstract
An apparatus and method for compressive sensing tap
identification for channel estimation comprising identifying a set
of significant taps in the time domain; representing a time-flat
channel response using a Taylor series expansion with the set of
significant taps; converting the time-flat channel response to a
vectorized channel response; transforming the vectorized channel
response to a compressive sensing (CS) polynomial frequency
response; aggregating the CS polynomial frequency response into a
stacked frequency response; converting the stacked frequency
response into a measured pilot frequency response; estimating a
channel parameter vector based on the measured pilot frequency
response; and generating a reconstructed channel response from the
channel parameter vector.
Inventors: |
Abrishamkar; Farrokh (San
Diego, CA), Wang; Ni-Chun (San Diego, CA), Shental;
Ori (Haifa, IL), Yu; Yingqun (San Diego, CA) |
Applicant: |
Name |
City |
State |
Country |
Type |
Abrishamkar; Farrokh
Wang; Ni-Chun
Shental; Ori
Yu; Yingqun |
San Diego
San Diego
Haifa
San Diego |
CA
CA
N/A
CA |
US
US
IL
US |
|
|
Assignee: |
QUALCOMM, Incorporated (San
Diego, CA)
|
Family
ID: |
43857833 |
Appl.
No.: |
12/950,452 |
Filed: |
November 19, 2010 |
Prior Publication Data
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Document
Identifier |
Publication Date |
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US 20110286558 A1 |
Nov 24, 2011 |
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Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
Issue Date |
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61263891 |
Nov 24, 2009 |
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61263885 |
Nov 24, 2009 |
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Current U.S.
Class: |
375/260; 375/267;
375/259; 375/262; 375/316; 375/295 |
Current CPC
Class: |
H04L
25/0234 (20130101); H04L 25/0242 (20130101); H04L
5/0048 (20130101); H04L 27/2607 (20130101) |
Current International
Class: |
H04K
1/10 (20060101) |
Field of
Search: |
;375/259,260,261,262,267,271,295,299,301,316,340,342 |
References Cited
[Referenced By]
U.S. Patent Documents
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Primary Examiner: Singh; Hirdepal
Attorney, Agent or Firm: Braden; Stanton
Parent Case Text
CLAIM OF PRIORITY UNDER 35 U.S.C. 119
The present Application for Patent claims priority to Provisional
Application No. 61/263,885 entitled Channel Estimation Using
Compressive Sensing for LTE and WiMax filed Nov. 24, 2009, and
WiMax filed Nov. 24, 2009, and 61/263,891, entitled CSCE Using
Taylor Series Expansion, filed Nov. 24, 2009, and assigned to the
assignee hereof and hereby expressly incorporated by reference
herein.
Claims
The invention claimed is:
1. A method for compressive sensing tap identification for channel
estimation comprising: identifying a set of significant taps in the
time domain; representing a time-flat channel response using a
Taylor series expansion with the set of significant taps;
converting the time-flat channel response to a vectorized channel
response; transforming the vectorized channel response to a
compressive sensing (CS) polynomial frequency response; aggregating
the CS polynomial frequency response into a stacked frequency
response; converting the stacked frequency response into a measured
pilot frequency response; estimating a channel parameter vector
based on the measured pilot frequency response; and generating a
reconstructed channel response from the channel parameter
vector.
2. The method of claim 1 further comprising generating a channel
spectrum from a channel response of a length D and a Fourier
matrix.
3. The method of claim 2 wherein the Fourier matrix is either a
discrete Fourier transform matrix or a fast Fourier transform
matrix.
4. The method of claim 2 further comprising obtaining a pilot
spectrum for a symbol time and a plurality of subcarriers from the
channel spectrum based on a pilot matrix.
5. The method of claim 4 further comprising generating a sensing
matrix based on the Fourier matrix F and the pilot matrix.
6. The method of claim 5 wherein the sensing matrix is a product of
the Fourier matrix and the pilot matrix.
7. The method of claim 5 further comprising augmenting the pilot
spectrum with other pilots.
8. The method of claim 7 wherein the other pilots are obtained for
a same subcarrier from the plurality of subcarriers.
9. The method of claim 1 further comprising using a tapped Fourier
matrix to transform the vectorized channel response to the
compressive sensing (CS) polynomial frequency response.
10. The method of claim 1 wherein the set of significant taps is
identified by using a L1 norm minimization recovery on a channel
impulse response.
11. An apparatus for compressive sensing tap identification for
channel estimation comprising: means for identifying a set of
significant taps in the time domain; means for representing a
time-flat channel response using a Taylor series expansion with the
set of significant taps; means for converting the time-flat channel
response to a vectorized channel response; means for transforming
the vectorized channel response to a compressive sensing (CS)
polynomial frequency response; means for aggregating the CS
polynomial frequency response into a stacked frequency response;
means for converting the stacked frequency response into a measured
pilot frequency response; means for estimating a channel parameter
vector based on the measured pilot frequency response; and means
for generating a reconstructed channel response from the channel
parameter vector.
12. The apparatus of claim 11 further comprising means for
generating a channel spectrum from a channel response of a length D
and a Fourier matrix.
13. The apparatus of claim 12 wherein the Fourier matrix is either
a discrete Fourier transform matrix or a fast Fourier transform
matrix.
14. The apparatus of claim 12 further comprising means for
obtaining a pilot spectrum for a symbol time and a plurality of
subcarriers from the channel spectrum based on a pilot matrix.
15. The apparatus of claim 14 further comprising means for
generating a sensing matrix based on the Fourier matrix F and the
pilot matrix.
16. The apparatus of claim 15 wherein the sensing matrix is a
product of the Fourier matrix and the pilot matrix.
17. The apparatus of claim 15 further comprising means for
augmenting the pilot spectrum with other pilots.
18. The apparatus of claim 17 wherein the other pilots are obtained
for a same subcarrier from the plurality of subcarriers.
19. The apparatus of claim 11 further comprising means for using a
tapped Fourier matrix to transform the vectorized channel response
to the compressive sensing (CS) polynomial frequency response.
20. The apparatus of claim 11 wherein the set of significant taps
is identified by using a L1 norm minimization recovery on a channel
impulse response.
21. An apparatus for compressive sensing tap identification for
channel estimation comprising a processor and a memory, the memory
containing program code executable by the processor for performing
the following: identifying a set of significant taps in the time
domain; representing a time-flat channel response using a Taylor
series expansion with the set of significant taps; converting the
time-flat channel response to a vectorized channel response;
transforming the vectorized channel response to a compressive
sensing (CS) polynomial frequency response; aggregating the CS
polynomial frequency response into a stacked frequency response;
converting the stacked frequency response into a measured pilot
frequency response; estimating a channel parameter vector based on
the measured pilot frequency response; and generating a
reconstructed channel response from the channel parameter
vector.
22. The apparatus of claim 21 wherein the memory further comprising
program code for generating a channel spectrum from a channel
response of a length D and a Fourier matrix.
23. The apparatus of claim 22 wherein the Fourier matrix is either
a discrete Fourier transform matrix or a fast Fourier transform
matrix.
24. The apparatus of claim 22 wherein the memory further comprising
program code for obtaining a pilot spectrum for a symbol time and a
plurality of subcarriers from the channel spectrum based on a pilot
matrix.
25. The apparatus of claim 24 wherein the memory further comprising
program code for generating a sensing matrix based on the Fourier
matrix F and the pilot matrix.
26. The apparatus of claim 25 wherein the sensing matrix is a
product of the Fourier matrix and the pilot matrix.
27. The apparatus of claim 25 wherein the memory further comprising
program code for augmenting the pilot spectrum with other
pilots.
28. The apparatus of claim 27 wherein the other pilots are obtained
for a same subcarrier from the plurality of subcarriers.
29. The apparatus of claim 21 wherein the memory further comprising
program code for using a tapped Fourier matrix to transform the
vectorized channel response to the compressive sensing (CS)
polynomial frequency response.
30. The apparatus of claim 21 wherein the set of significant taps
is identified by using a L1 norm minimization recovery on a channel
impulse response.
31. A non-transitory computer-readable medium for compressive
sensing tap identification for channel estimation, the
computer-readable medium storing a computer program, wherein
execution of the computer program is for: identifying a set of
significant taps in the time domain; representing a time-flat
channel response using a Taylor series expansion with the set of
significant taps; converting the time-flat channel response to a
vectorized channel response; transforming the vectorized channel
response to a compressive sensing (CS) polynomial frequency
response; aggregating the CS polynomial frequency response into a
stacked frequency response; converting the stacked frequency
response into a measured pilot frequency response; estimating a
channel parameter vector based on the measured pilot frequency
response; and generating a reconstructed channel response from the
channel parameter vector.
32. The computer-readable medium of claim 31 wherein execution of
the computer program is also for generating a channel spectrum from
a channel response of a length D and a Fourier matrix.
33. The computer-readable medium of claim 32 wherein the Fourier
matrix is either a discrete Fourier transform matrix or a fast
Fourier transform matrix.
34. The computer-readable medium of claim 32 wherein execution of
the computer program is also for obtaining a pilot spectrum for a
symbol time and a plurality of subcarriers from the channel
spectrum based on a pilot matrix.
35. The computer-readable medium of claim 34 wherein execution of
the computer program is also for generating a sensing matrix based
on the Fourier matrix F and the pilot matrix.
36. The computer-readable medium of claim 35 wherein the sensing
matrix is a product of the Fourier matrix and the pilot matrix.
37. The computer-readable medium of claim 35 wherein execution of
the computer program is also for augmenting the pilot spectrum with
other pilots.
38. The computer-readable medium of claim 37 wherein the other
pilots are obtained for a same subcarrier from the plurality of
subcarriers.
39. The computer-readable medium of claim 31 wherein execution of
the computer program is also for using a tapped Fourier matrix to
transform the vectorized channel response to the compressive
sensing (CS) polynomial frequency response.
40. The computer-readable medium of claim 31 wherein the set of
significant taps is identified by using a L1 norm minimization
recovery on a channel impulse response.
Description
FIELD
This disclosure relates generally to apparatus and methods for
channel estimation in wireless communication. More particularly,
the disclosure relates to compressive sensing tap identification
for channel estimation.
BACKGROUND
Wireless communication systems are widely deployed to provide
various types of communication content such as voice, data, and so
on. These systems may be multiple-access systems capable of
supporting communication with multiple users by sharing the
available system resources (e.g., bandwidth and transmit power).
Examples of such multiple-access systems include code division
multiple access (CDMA) systems, time division multiple access
(TDMA) systems, frequency division multiple access (FDMA) systems,
3.sup.rd Generation Partnership Project (3GPP) Long Term Evolution
(LTE) systems, and orthogonal frequency division multiple access
(OFDMA) systems.
Generally, a wireless multiple-access communication system can
simultaneously support communication for multiple wireless
terminals. Each terminal communicates with one or more base
stations via transmissions on the forward and reverse links. The
forward link (or downlink) refers to the communication link from
the base stations to the terminals, and the reverse link (or
uplink) refers to the communication link from the terminals to the
base stations. This communication link may be established via a
single-input single-output (SISO), multiple-input single-output
(MISO) or a multiple-input multiple-output (MIMO) system.
SUMMARY
Disclosed is an apparatus and method for compressive sensing tap
identification for channel estimation. According to one aspect, a
method for compressive sensing tap identification for channel
estimation comprising identifying a set of significant taps in the
time domain; representing a time-flat channel response using a
Taylor series expansion with the set of significant taps;
converting the time-flat channel response to a vectorized channel
response; transforming the vectorized channel response to a
compressive sensing (CS) polynomial frequency response; aggregating
the CS polynomial frequency response into a stacked frequency
response; converting the stacked frequency response into a measured
pilot frequency response; estimating a channel parameter vector
based on the measured pilot frequency response; and generating a
reconstructed channel response from the channel parameter
vector.
According to another aspect, an apparatus for compressive sensing
tap identification for channel estimation comprising means for
identifying a set of significant taps in the time domain; means for
representing a time-flat channel response using a Taylor series
expansion with the set of significant taps; means for converting
the time-flat channel response to a vectorized channel response;
means for transforming the vectorized channel response to a
compressive sensing (CS) polynomial frequency response; means for
aggregating the CS polynomial frequency response into a stacked
frequency response; means for converting the stacked frequency
response into a measured pilot frequency response; means for
estimating a channel parameter vector based on the measured pilot
frequency response; and means for generating a reconstructed
channel response from the channel parameter vector.
According to another aspect, an apparatus for compressive sensing
tap identification for channel estimation comprising a processor
and a memory, the memory containing program code executable by the
processor for performing the following: identifying a set of
significant taps in the time domain; representing a time-flat
channel response using a Taylor series expansion with the set of
significant taps; converting the time-flat channel response to a
vectorized channel response; transforming the vectorized channel
response to a compressive sensing (CS) polynomial frequency
response; aggregating the CS polynomial frequency response into a
stacked frequency response; converting the stacked frequency
response into a measured pilot frequency response; estimating a
channel parameter vector based on the measured pilot frequency
response; and generating a reconstructed channel response from the
channel parameter vector
According to another aspect, a computer-readable medium for
compressive sensing tap identification for channel estimation, the
computer-readable medium storing a computer program, wherein
execution of the computer program is for: identifying a set of
significant taps in the time domain; representing a time-flat
channel response using a Taylor series expansion with the set of
significant taps; converting the time-flat channel response to a
vectorized channel response; transforming the vectorized channel
response to a compressive sensing (CS) polynomial frequency
response; aggregating the CS polynomial frequency response into a
stacked frequency response; converting the stacked frequency
response into a measured pilot frequency response; estimating a
channel parameter vector based on the measured pilot frequency
response; and generating a reconstructed channel response from the
channel parameter vector.
Advantages of the present disclosure may include increase
performance and efficiency in the case of channel sparsity.
It is understood that other aspects will become readily apparent to
those skilled in the art from the following detailed description,
wherein it is shown and described various aspects by way of
illustration. The drawings and detailed description are to be
regarded as illustrative in nature and not as restrictive.
BRIEF DESCRIPTION OF THE DRAWINGS
FIG. 1 illustrates an example multiple access wireless
communication system according to one example.
FIG. 2 illustrates an example block diagram of a transmitter system
(also known as access point) and a receiver system (also known as
access terminal) in a multiple input multiple output (MIMO)
system.
FIG. 3 illustrates an example cell-specific RS arrangement with a
normal cyclic prefix (CP) length.
FIG. 4 illustrates an example of an augmented pilot measurement
using a neighboring OFDM symbol time.
FIG. 5 illustrates an example of a flow diagram for compressive
sensing tap identification for channel estimation.
FIG. 6 illustrates an example of a device comprising a processor in
communication with a memory for executing the processes for
compressive sensing tap identification for channel estimation.
FIG. 7 illustrates an example of a device suitable for compressive
sensing tap identification for channel estimation.
DETAILED DESCRIPTION
The detailed description set forth below in connection with the
appended drawings is intended as a description of various aspects
of the present disclosure and is not intended to represent the only
aspects in which the present disclosure may be practiced. Each
aspect described in this disclosure is provided merely as an
example or illustration of the present disclosure, and should not
necessarily be construed as preferred or advantageous over other
aspects. The detailed description includes specific details for the
purpose of providing a thorough understanding of the present
disclosure. However, it will be apparent to those skilled in the
art that the present disclosure may be practiced without these
specific details. In some instances, well-known structures and
devices are shown in block diagram form in order to avoid obscuring
the concepts of the present disclosure. Acronyms and other
descriptive terminology may be used merely for convenience and
clarity and are not intended to limit the scope of the present
disclosure.
While for purposes of simplicity of explanation, the methodologies
are shown and described as a series of acts, it is to be understood
and appreciated that the methodologies are not limited by the order
of acts, as some acts may, in accordance with one or more aspects,
occur in different orders and/or concurrently with other acts from
that shown and described herein. For example, those skilled in the
art will understand and appreciate that a methodology could
alternatively be represented as a series of interrelated states or
events, such as in a state diagram. Moreover, not all illustrated
acts may be required to implement a methodology in accordance with
one or more aspects.
The techniques described herein may be used for various wireless
communication networks such as Code Division Multiple Access (CDMA)
networks, Time Division Multiple Access (TDMA) networks, Frequency
Division Multiple Access (FDMA) networks, Orthogonal FDMA (OFDMA)
networks, Single-Carrier FDMA (SC-FDMA) networks, etc. The terms
"networks" and "systems" are often used interchangeably. A CDMA
network may implement a radio technology such as Universal
Terrestrial Radio Access (UTRA), cdma2000, etc. UTRA includes
Wideband-CDMA (W-CDMA) and Low Chip Rate (LCR). Cdma2000 covers
IS-2000, IS-95 and IS-856 standards. A TDMA network may implement a
radio technology such as Global System for Mobile Communications
(GSM). An OFDMA network may implement a radio technology such as
Evolved UTRA (E-UTRA), IEEE 802.11, IEEE 802.16, IEEE 802.20,
Flash-OFDM.RTM., etc. UTRA, E-UTRA, and GSM are part of Universal
Mobile Telecommunication System (UMTS). Long Term Evolution (LTE)
is an upcoming release of UMTS that uses E-UTRA. UTRA, E-UTRA, GSM,
UMTS and LTE are described in documents from an organization named
"3rd Generation Partnership Project" (3GPP). cdma2000 is described
in documents from an organization named "3rd Generation Partnership
Project 2" (3GPP2). These various radio technologies and standards
are known in the art. For clarity, certain aspects of the
techniques are described below for LTE, and LTE terminology is used
in much of the description below.
Single carrier frequency division multiple access (SC-FDMA), which
utilizes single carrier modulation and frequency domain
equalization is a transmission technique. SC-FDMA has similar
performance and essentially the same overall complexity as those of
an OFDMA system. SC-FDMA signal has lower peak-to-average power
ratio (PAPR) because of its inherent single carrier structure.
SC-FDMA has drawn great attention, especially in the uplink
communications where lower PAPR greatly benefits the mobile
terminal in terms of transmit power efficiency. It is currently a
working assumption for uplink multiple access scheme in 3GPP Long
Term Evolution (LTE), or Evolved UTRA.
Referring to FIG. 1, a multiple access wireless communication
system according to one embodiment is illustrated. An access point
100 (AP) includes multiple antenna groups, one including 104 and
106, another including 108 and 110, and an additional including 112
and 114. In FIG. 1, only two antennas are shown for each antenna
group, however, more or fewer antennas may be utilized for each
antenna group. Access terminal 116 (AT) is in communication with
antennas 112 and 114, where antennas 112 and 114 transmit
information to access terminal 116 over forward link 120 and
receive information from access terminal 116 over reverse link 118.
Access terminal 122 is in communication with antennas 106 and 108,
where antennas 106 and 108 transmit information to access terminal
122 over forward link 126 and receive information from access
terminal 122 over reverse link 124. In a FDD system, communication
links 118, 120, 124 and 126 may use different frequency for
communication. For example, forward link 120 may use a different
frequency then that used by reverse link 118.
Each group of antennas and/or the area in which they are designed
to communicate is often referred to as a sector of the access
point. In the embodiment, antenna groups each are designed to
communicate to access terminals in a sector of the areas covered by
access point 100.
In communication over forward links 120 and 126, the transmitting
antennas of access point 100 utilize beamforming in order to
improve the signal-to-noise ratio of forward links for the
different access terminals 116 and 124. Also, an access point using
beamforming to transmit to access terminals scattered randomly
through its coverage causes less interference to access terminals
in neighboring cells than an access point transmitting through a
single antenna to all its access terminals.
An access point may be a fixed station used for communicating with
the terminals and may also be referred to as an access point, a
Node B, an eNodeB or some other terminology. An access terminal may
also be called a mobile terminal, a mobile device, a user equipment
(UE), a wireless communication device, terminal, access terminal or
some other terminology.
FIG. 2 illustrates an example block diagram of a transmitter system
210 (also known as access point) and a receiver system 250 (also
known as access terminal) in a multiple input multiple output
(MIMO) system 200. At the transmitter system 210, traffic data for
a number of data streams is provided from a data source 212 to a
transmit (TX) data processor 214.
In an embodiment, each data stream is transmitted over a respective
transmit antenna. TX data processor 214 formats, codes, and
interleaves the traffic data for each data stream based on a
particular coding scheme selected for that data stream to provide
coded data.
The coded data for each data stream may be multiplexed with pilot
data using OFDM techniques. The pilot data is typically a known
data pattern that is processed in a known manner and may be used at
the receiver system to estimate the channel response. The
multiplexed pilot and coded data for each data stream is then
modulated (i.e., symbol mapped) based on a particular modulation
scheme (e.g., BPSK, QSPK, M-PSK, or M-QAM) selected for that data
stream to provide modulation symbols. The data rate, coding, and
modulation for each data stream may be determined by instructions
performed by processor 230.
The modulation symbols for all data streams are then provided to a
TX MIMO processor 220, which may further process the modulation
symbols (e.g., for OFDM). TX MIMO processor 220 then provides
N.sub.T modulation symbol streams to N.sub.T transmitters (TMTR)
222a through 222t. In certain embodiments, TX MIMO processor 220
applies beamforming weights to the symbols of the data streams and
to the antenna from which the symbol is being transmitted.
Each transmitter 222 receives and processes a respective symbol
stream to provide one or more analog signals, and further
conditions (e.g., amplifies, filters, and upconverts) the analog
signals to provide a modulated signal suitable for transmission
over the MIMO channel. N.sub.T modulated signals from transmitters
222a through 222t are then transmitted from N.sub.T antennas 224a
through 224t, respectively.
At receiver system 250, the transmitted modulated signals are
received by N.sub.R antennas 252a through 252r and the received
signal from each antenna 252 is provided to a respective receiver
(RCVR) 254a through 254r. Each receiver 254 conditions (e.g.,
filters, amplifies, and downconverts) a respective received signal,
digitizes the conditioned signal to provide samples, and further
processes the samples to provide a corresponding "received" symbol
stream.
An RX data processor 260 then receives and processes the N.sub.R
received symbol streams from N.sub.R receivers 254 based on a
particular receiver processing technique to provide N.sub.T
"detected" symbol streams. The RX data processor 260 then
demodulates, deinterleaves, and decodes each detected symbol stream
to recover the traffic data for the data stream. The processing by
RX data processor 260 is complementary to that performed by TX MIMO
processor 220 and TX data processor 214 at transmitter system
210.
A processor 270 periodically determines which pre-coding matrix to
use (discussed below). Processor 270 formulates a reverse link
message comprising a matrix index portion and a rank value
portion.
The reverse link message may comprise various types of information
regarding the communication link and/or the received data stream.
The reverse link message is then processed by a TX data processor
238, which also receives traffic data for a number of data streams
from a data source 236, modulated by a modulator 280, conditioned
by transmitters 254a through 254r, and transmitted back to
transmitter system 210.
At transmitter system 210, the modulated signals from receiver
system 250 are received by antennas 224, conditioned by receivers
222, demodulated by a demodulator 240, and processed by a RX data
processor 242 to extract the reserve link message transmitted by
the receiver system 250. Processor 230 then determines which
pre-coding matrix to use for determining the beamforming weights
then processes the extracted message.
In one aspect, the LTE downlink provides reference signals (RSs),
i.e., pilots, within certain locations within an OFDM
time-frequency lattice. For example, FIG. 3 illustrates an example
cell-specific RS arrangement with a normal cyclic prefix (CP)
length. As shown, the RS symbols are shown staggered in the time
dimension and frequency dimension according to the expected channel
coherence bandwidth and maximum Doppler spread, respectively.
In an aspect, logical channels are classified into Control Channels
and Traffic Channels. Logical Control Channels comprise Broadcast
Control Channel (BCCH) which is DL channel for broadcasting system
control information, Paging Control Channel (PCCH) which is DL
channel that transfers paging information, and Multicast Control
Channel (MCCH) which is Point-to-multipoint DL channel used for
transmitting Multimedia Broadcast and Multicast Service (MBMS)
scheduling and control information for one or several MTCHs.
Generally, after establishing RRC connection this channel is only
used by UEs that receive MBMS (Note: old MCCH+MSCH). Dedicated
Control Channel (DCCH) is Point-to-point bi-directional channel
that transmits dedicated control information and used by UEs having
an RRC connection. In one aspect, Logical Traffic Channels
comprises a Dedicated Traffic Channel (DTCH) which is
Point-to-point bi-directional channel, dedicated to one UE, for the
transfer of user information. Also, a Multicast Traffic Channel
(MTCH) for Point-to-multipoint DL channel for transmitting traffic
data.
In an aspect, Transport Channels are classified into downlink (DL)
and uplink (UL). DL Transport Channels comprise a Broadcast Channel
(BCH), Downlink Shared Data Channel (DL-SDCH) and a Paging Channel
(PCH), the PCH for support of UE power saving (DRX cycle is
indicated by the network to the UE), broadcasted over entire cell
and mapped to PHY resources which can be used for other
control/traffic channels. The UL Transport Channels comprise a
Random Access Channel (RACH), a Request Channel (REQCH), a Uplink
Shared Data Channel (UL-SDCH) and plurality of PHY channels. The
PHY channels comprise a set of DL channels and UL channels.
In one aspect, the DL PHY channels may comprise one or more of the
following:
TABLE-US-00001 Common Pilot Channel (CPICH) Synchronization Channel
(SCH) Common Control Channel (CCCH) Shared DL Control Channel
(SDCCH) Multicast Control Channel (MCCH) Shared UL Assignment
Channel (SUACH) Acknowledgement Channel (ACKCH) DL Physical Shared
Data Channel (DL-PSDCH) UL Power Control Channel (UPCCH) Paging
Indicator Channel (PICH) Load Indicator Channel (LICH) In one
aspect, the UL PHY channels may comprise one or more of the
following: Physical Random Access Channel (PRACH) Channel Quality
Indicator Channel (CQICH) Acknowledgement Channel (ACKCH) Antenna
Subset Indicator Channel (ASICH) Shared Request Channel (SREQCH) UL
Physical Shared Data Channel (UL-PSDCH) Broadband Pilot Channel
(BPICH)
In one aspect, a channel structure is provided that preserves low
peak to average power ratio (PAPR) properties of a single carrier
waveform (i.e., at any given time, the channel is contiguous or
uniformly spaced in frequency).
For the purposes of the present disclosure, one or more of the
following abbreviations may apply:
TABLE-US-00002 AM Acknowledged Mode AMD Acknowledged Mode Data ARQ
Automatic Repeat Request BCCH Broadcast Control CHannel BCH
Broadcast CHannel C- Control- CCCH Common Control CHannel CCH
Control CHannel CCTrCH Coded Composite Transport Channel CoMP
coordinated multi point CP Cyclic Prefix CRC Cyclic Redundancy
Check CTCH Common Traffic CHannel DCCH Dedicated Control CHannel
DCH Dedicated CHannel DL DownLink DL-SCH downlink shared channel
DSCH Downlink Shared CHannel DTCH Dedicated Traffic Channel DCI
Downlink Control Information FACH Forward link Access CHannel FDD
Frequency Division Duplex L1 Layer 1 (physical layer) L2 Layer 2
(data link layer) L3 Layer 3 (network layer) LI Length Indicator
LSB Least Significant Bit LTE Long Term Evolution LTE-A
LTE-Advanced or Long Term Evolution - Advanced MAC Medium Access
Control MBMS Multimedia Broadcast Multicast Service MBSFN multicast
broadcast single frequency network MCCH MBMS point-to-multipoint
Control CHannel MCE MBMS coordinating entity MCH multicast channel
MRW Move Receiving Window MSB Most Significant Bit MSCH MBMS
point-to-multipoint Scheduling Channel (depending on context) MSCH
MBMS control channel (depending on context) MTCH MBMS
point-to-multipoint Traffic CHannel PBCH Physical Broadcast CHannel
PCCH Paging Control CHannel PCFICH Physical Control Format
Indicator Channel PCH Paging CHannel PDCCH physical downlink
control channel PDSCH physical downlink shared channel PDU Protocol
Data Unit PHICH Physical Hybrid ARQ Indicator CHannel PHY PHYsical
layer PhyCH Physical Channels PMCH Physical Multicast Channel PRACH
Physical Random Access Channel PUCCH Physical Uplink Control
Channel PUSCH Physical Uplink Shared Channel RACH Random Access
CHannel RLC Radio Link Control RRC Radio Resource Control SAP
Service Access Point SDU Service Data Unit SHCCH SHared channel
Control CHannel SN Sequence Number SNR signal-to-noise ratio SUFI
SUper FIeld TCH Traffic CHannel TDD Time Division Duplex TFI
Transport Format Indicator TM Transparent Mode TMD Transparent Mode
Data TTI Transmission Time Interval U- User- UE User Equipment UL
UpLink UM Unacknowledged Mode UMD Unacknowledged Mode Data UMTS
Universal Mobile Telecommunications System UTRA UMTS Terrestrial
Radio Access UTRAN UMTS Terrestrial Radio Access Network
Long Term Evolution (LTE) is a next-generation evolution of the
Universal Mobile Telecommunications System (UMTS), a worldwide
protocol family for wireless communications. LTE provides several
new technological features compared to previous wireless
technologies including OFDM multicarrier transmission, provisions
for multiple antennas for both transmit and receive, and an
Internet protocol (IP) packet switching network infrastructure. In
particular, OFDM relies on a two-dimensional array of orthogonal
time and frequency resources which may be aggregated in many
flexible ways to provide a wide variety of user services.
In one aspect, a mobile station or mobile terminal that a user
carries for wireless communications is known as user equipment
(UE). In general, the UE connects to other users either within the
wireless network or the general communications infrastructure such
as the public switched telephony network (PSTN), Internet, private
networks, wide area networks (WANs), etc. via a wireless
bidirectional link to an evolved NodeB (eNodeB), also known
generically as a base station, which represents the wireless
network access node for the UE. Other wireless network elements
separate from the access nodes (e.g. eNodeBs) are considered part
of the core network (CN). The eNodeB is connected to other network
elements such as the serving gateway (S-GW) and the Mobility
Management Entity (MME). In one aspect, the S-GW serves as a
mobility anchor for data bearers when the UE moves between
different eNodeBs. In another aspect, the MME serves as a control
entity for managing the signaling between the UE and the core
network (CN). The S-GW interfaces with the packet data network
gateway (P-GW), which functions as a LTE portal to the global
Internet, for example. The P-GW also allocates IP addresses for the
UE and enforces quality of service (QoS) based on policy rules.
In one aspect, the downlink resources in LTE are partitioned into
smaller elemental time and frequency resources. In one example, in
the time dimension, a radio frame has 10 ms duration and is divided
into ten subframes, each of 1 ms duration. Furthermore, each
subframe is divided into two 0.5 ms slots. In the case of a normal
cyclic prefix length, each slot comprises seven OFDM symbols. In
the frequency dimension, a Resource Block (RB) is a group of 12
subcarriers each with a subcarrier bandwidth of 15 kHz. A
subcarrier is also denoted as a tone, for example. One Resource
Element (RE) is the smallest resource unit in LTE which consists of
one subcarrier and one OFDM symbol.
In another aspect, certain Resource Blocks are dedicated for
special signals such as synchronization signals, reference signals,
control signals and broadcast system information. For example,
three essential synchronization steps in LTE may be necessary:
symbol timing acquisition, carrier frequency synchronization, and
sampling clock synchronization. In one example, LTE relies on two
special synchronization signals for each cell: the Primary
Synchronization Signal (PSS) and the Secondary Synchronization
Signal (SSS) which are used for time and frequency synchronization
and for broadcasting of certain system parameters such as cell
identification, cyclic prefix length, duplex method, etc. In
general, the PSS is detected by the UE first, followed by SSS
detection.
In one aspect, the PSS is based on a Zadoff-Chu sequence, a
constant amplitude chirp-like digital sequence. In general, the PSS
is detected non-coherently (i.e., detection without phase
information) by the UE since there is assumed to be no a priori
channel information available by the UE. In another aspect, the SSS
is based on a maximal length sequence (also known as M-sequence).
Since the detection of the SSS is performed after the detection of
the PSS, if channel state information (CSI) is available to the UE
after PSS detection, then coherent detection (i.e., detection with
phase information) of the SSS may be available. In certain
scenarios, however, non-coherent detection of the SSS may be
required, for example, in the case of coherent interference from
neighboring eNodeBs.
A MIMO system employs multiple (N.sub.T) transmit antennas and
multiple (N.sub.R) receive antennas for data transmission. A MIMO
channel formed by the N.sub.T transmit and N.sub.R receive antennas
may be decomposed into N.sub.S independent channels, which are also
referred to as spatial channels, where N.sub.S.ltoreq.min{N.sub.T,
N.sub.R}. Each of the N.sub.S independent channels corresponds to a
dimension. The MIMO system may provide improved performance (e.g.,
higher throughput and/or greater reliability) if the additional
dimensionalities created by the multiple transmit and receive
antennas are utilized.
A MIMO system supports time division duplex (TDD) and frequency
division duplex (FDD) systems. In a TDD system, the forward and
reverse link transmissions are on the same frequency region so that
the reciprocity principle allows the estimation of the forward link
channel from the reverse link channel. This enables the access
point to extract transmit beamforming gain on the forward link when
multiple antennas are available at the access point.
Channel estimation in OFDM wireless system may employ multiple
amplitude signaling schemes that track fading radio channels. OFDM
is a significant modulation technique for digital communication on
mobile multipath fading channels. In one example, to perform
coherent demodulation on the received signals it is necessary to
have knowledge of the time-varying channel transfer function.
For OFDM systems, the channel transfer function may be conveniently
estimated using a two dimensional grid of pilot symbols, that is,
over symbol time and discrete frequency tone. The Digital Video
Broadcasting Terrestrial (DVB-T) standard is one such example.
However, channel capacity is wasted due to the transmission of the
pilot symbols in these systems.
One alternative is to use differential phase shift keying (DPSK)
and differentially coherent demodulation to obviate the need for
channel estimation. For example, DPSK has been successfully
implemented in the Digital Audio Broadcasting standard. However,
differential detection results in a bit energy to noise density
ratio penalty of, for example, approximately 2 dB for an additive
white Gaussian network (AWGN) channel and a larger loss for fading
channels.
In one aspect, it is desirable to enable coherent demodulation
while implementing channel estimation without the need for pilot
symbols. One technique, known as blind channel estimation, has been
employed, but its performance has not been comparable to that of
pilot-based channel estimation.
In one aspect, a channel matrix is used as a model for the channel
propagation characteristics between the transmitter and receiver.
Channel estimation then refers to the estimation of the parameters
of the channel matrix. Most existing techniques for channel
estimation use minimum mean square error (MMSE) or least square
(LS) techniques. However, these estimation techniques do not take
advantage of channel sparsity, thereby resulting in significant
performance loss. In one aspect, compressive sensing (CS)
techniques may be used to perform channel estimation under the
condition of sparsity, that is, when the channel matrix is
comprised of mostly zeros.
Thus, it is desirable to derive channel estimation techniques which
outperform current non-compressive sensing approaches for both LTE
and WiMax systems, especially for high Doppler fading channels and
which also require significantly less pilot symbols.
In one aspect, a compressive sensing tap identification technique
for channel estimation in OFDM wireless systems attains superior
performance for narrowband OFDM systems, for example, WiMax and
LTE. In one example, this technique provides better compressive
sensing performance for OFDM channel estimation than existing
techniques.
In one aspect, a compressive sensing tap identification technique
for channel estimation first uses L1 norm minimization to identify
active taps for a channel response and then uses conventional L2
norm minimization to estimate active tap values.
In one example, a time domain response h having length D may be
represented in the frequency domain as
.function. ##EQU00001## where H is a channel spectrum corresponding
to a channel impulse response h and F.sub.kxD is a Fourier matrix.
Alternatively, h may be viewed as the projection of H along the
column space, i.e. the basis, of the discrete Fourier transform
(DFT) matrix: X=.PSI.s where .PSI. is the discrete Fourier
transform matrix, s is a time domain vector and X is a frequency
domain vector.
In one aspect, pilot measurements may be performed according to the
following equation to obtain a pilot spectrum from the channel
spectrum:
.PHI..function..PHI..times..times.
.function..revreaction..times..times. ##EQU00002## where .PHI. is a
pilot matrix. In one aspect, since the number of pilots is less
than the time domain vector length D, the time domain vector s is
sparse and the sensing matrix W follows a uniform uncertainty
principle (UUP), compressed sensing may be used to identify
significant taps in the channel model.
In another aspect, the pilot spectrum .PHI.H may be augmented. In
one example, to increase the pilots per OFDM symbol time, one may
borrow from a neighboring OFDM symbol time for the same subcarrier.
FIG. 4 illustrates an example of an augmented pilot measurement
using a neighboring OFDM symbol time. For example, the channel
impulse response h may be solved by L1 norm recovery to identify
significant taps in the time domain.
In one aspect, compressive sensing identified taps may be
represented by a polynomial model. For example, if .OMEGA. denotes
the set of CS-identified taps, then each survived tap may be used
to construct a time-flat channel response, with polynomial order
Q:
.function..times..alpha..times..di-elect cons..OMEGA.
##EQU00003##
In one example, the time-flat channel response may be converted
into a vectorized channel response as follows:
.OMEGA..function..times..alpha..OMEGA..times. ##EQU00004##
In another aspect, the vectorized channel response may be converted
into a CS polynomial frequency response G(1) as follows:
.function..times..OMEGA..times..function..times..times..OMEGA..times..alp-
ha..OMEGA..times..times..times..OMEGA..times..times..OMEGA..times..times..-
times..times..OMEGA..function..alpha..OMEGA..alpha.
##EQU00005##
Next, the CS polynomial frequency response may be aggregated over L
symbols to form a stacked frequency response G as follows:
.function..function..times..times..OMEGA..times..times..OMEGA..times..tim-
es..OMEGA..times..times..OMEGA..times..times..OMEGA..times..times..OMEGA..-
times..times..OMEGA..times..times..OMEGA..times..times..OMEGA.
.times..OMEGA..times..times..alpha..alpha.
.OMEGA..times..times..times..times..times. ##EQU00006##
.times..PSI..times..times..alpha. ##EQU00006.2##
In another aspect, the stacked frequency response G may be
converted into a measured pilot frequency response .PHI.G according
to: .PHI.G=.PHI..PSI..alpha. G.sub.pilot=W.alpha. Y=Ws Y:
JL.times.1, W: JL.times.|.OMEGA.|, s: |.OMEGA.|Q.times.1 J: number
of pilots per symbol In one example, channel parameters .alpha. may
be estimated from the measured pilot frequency response by a
least-square procedure and then used to reconstruct the channel
frequency response to form a reconstructed channel frequency
response G(1) where: C(l)=U
In one example, for WiMax narrowband cannel estimation, the number
of pilots J=2 and the number of symbols L=10. In one example, the
polynomial order Q may be selected between 2 and 4, depending on
the Doppler spread.
FIG. 5 illustrates an example of a flow diagram for compressive
sensing tap identification for channel estimation. In block 510,
generate a channel spectrum H from a channel response h of a length
D and a Fourier matrix F. In one example, the Fourier matrix F is a
discrete Fourier transform matrix. In another example, the Fourier
matrix is a fast Fourier transform matrix. In block 520, obtain a
pilot spectrum P for a symbol time and a plurality of subcarriers
from the channel spectrum H based on a pilot matrix .PHI.. In one
example, the number of pilots in pilot matrix .PHI. is less than
the channel impulse response length D. In block 530, generate a
sensing matrix W based on the Fourier matrix F and the pilot matrix
.PHI.. In one example, the sensing matrix W is a product of the
Fourier matrix F and the pilot matrix .PHI.. In block 540, augment
the pilot spectrum P with other pilots. In one example, the other
pilots are obtained from neighboring symbol times. In another
example, the pilots are obtained for a same subcarrier from the
plurality of subcarriers.
In block 550, identify a set of significant taps .OMEGA. in the
time domain. In one example, identify the set of significant taps
.OMEGA. by using L1 norm minimization recovery on the channel
impulse response h. In block 560, represent a time-flat channel
response h.sub.p(1) using a Taylor series expansion with the set of
significant taps .OMEGA.. In one example, the time-flat channel
response is modeled as a polynomial of order Q, wherein Q is an
integer of typically less than 4. However, one skilled in the art
would understand that Q can be any integer without affecting the
spirit or scope of the present disclosure.
In block 570, convert the time-flat channel response h.sub.p(1) to
a vectorized channel response. In block 580, transform the
vectorized channel response to a compressive sensing (CS)
polynomial frequency response. In one example, the CS polynomial
frequency response is transformed using a tapped Fourier matrix
F.sub.Kx.OMEGA.. In block 590, aggregate the CS polynomial
frequency response into a stacked frequency response G. In block
600, convert the stacked frequency response into a measured pilot
frequency response. In block 610, estimate channel parameter vector
.alpha. based on the measure pilot frequency response. In one
example, the channel parameter vector .alpha. may be estimated
using a least-square minimization. In block 620, generate a
reconstructed channel response G(1) from the channel parameter
vector .alpha..
One skilled in the art would understand that the steps disclosed in
the example flow diagram in FIG. 5 can be interchanged in their
order without departing from the scope and spirit of the present
disclosure. Also, one skilled in the art would understand that the
steps illustrated in the flow diagram are not exclusive and other
steps may be included or one or more of the steps in the example
flow diagram may be deleted without affecting the scope and spirit
of the present disclosure.
Those of skill would further appreciate that the various
illustrative components, logical blocks, modules, circuits, and/or
algorithm steps described in connection with the examples disclosed
herein may be implemented as electronic hardware, firmware,
computer software, or combinations thereof. To clearly illustrate
this interchangeability of hardware, firmware and software, various
illustrative components, blocks, modules, circuits, and/or
algorithm steps have been described above generally in terms of
their functionality. Whether such functionality is implemented as
hardware, firmware or software depends upon the particular
application and design constraints imposed on the overall system.
Skilled artisans may implement the described functionality in
varying ways for each particular application, but such
implementation decisions should not be interpreted as causing a
departure from the scope or spirit of the present disclosure.
For example, for a hardware implementation, the processing units
may be implemented within one or more application specific
integrated circuits (ASICs), digital signal processors (DSPs),
digital signal processing devices (DSPDs), programmable logic
devices (PLDs), field programmable gate arrays (FPGAs), processors,
controllers, micro-controllers, microprocessors, other electronic
units designed to perform the functions described therein, or a
combination thereof. With software, the implementation may be
through modules (e.g., procedures, functions, etc.) that perform
the functions described therein. The software codes may be stored
in memory units and executed by a processor unit. Additionally, the
various illustrative flow diagrams, logical blocks, modules and/or
algorithm steps described herein may also be coded as
computer-readable instructions carried on any computer-readable
medium known in the art or implemented in any computer program
product known in the art.
In one or more examples, the steps or functions described herein
may be implemented in hardware, software, firmware, or any
combination thereof. If implemented in software, the functions may
be stored on or transmitted over as one or more instructions or
code on a computer-readable medium. Computer-readable media
includes both computer storage media and communication media
including any medium that facilitates transfer of a computer
program from one place to another. A storage media may be any
available media that can be accessed by a computer. By way of
example, and not limitation, such computer-readable media can
comprise RAM, ROM, EEPROM, CD-ROM or other optical disk storage,
magnetic disk storage or other magnetic storage devices, or any
other medium that can be used to carry or store desired program
code in the form of instructions or data structures and that can be
accessed by a computer. Also, any connection is properly termed a
computer-readable medium. For example, if the software is
transmitted from a website, server, or other remote source using a
coaxial cable, fiber optic cable, twisted pair, digital subscriber
line (DSL), or wireless technologies such as infrared, radio, and
microwave, then the coaxial cable, fiber optic cable, twisted pair,
DSL, or wireless technologies such as infrared, radio, and
microwave are included in the definition of medium. Disk and disc,
as used herein, includes compact disc (CD), laser disc, optical
disc, digital versatile disc (DVD), floppy disk and blu-ray disc
where disks usually reproduce data magnetically, while discs
reproduce data optically with lasers. Combinations of the above
should also be included within the scope of computer-readable
media.
In one example, the illustrative components, flow diagrams, logical
blocks, modules and/or algorithm steps described herein are
implemented or performed with one or more processors. In one
aspect, a processor is coupled with a memory which stores data,
metadata, program instructions, etc. to be executed by the
processor for implementing or performing the various flow diagrams,
logical blocks and/or modules described herein. FIG. 6 illustrates
an example of a device 650 comprising a processor 660 in
communication with a memory 670 for executing the processes for
compressive sensing tap identification for channel estimation. In
one example, the device 650 is used to implement the algorithm
illustrated in FIG. 5. In one aspect, the memory 670 is located
within the processor 660. In another aspect, the memory 670 is
external to the processor 660. In one aspect, the processor
includes circuitry for implementing or performing the various flow
diagrams, logical blocks and/or modules described herein.
FIG. 7 illustrates an example of a device 700 suitable for
compressive sensing tap identification for channel estimation. In
one aspect, the device 700 is implemented by at least one processor
comprising one or more modules configured to provide different
aspects of compressive sensing tap identification for channel
estimation as described herein in blocks 710, 720, 730, 740, 750,
760, 770, 780, 790, 800, 810 and 820. For example, each module
comprises hardware, firmware, software, or any combination thereof.
In one aspect, the device 700 is also implemented by at least one
memory in communication with the at least one processor.
The previous description of the disclosed aspects is provided to
enable any person skilled in the art to make or use the present
disclosure. Various modifications to these aspects will be readily
apparent to those skilled in the art, and the generic principles
defined herein may be applied to other aspects without departing
from the spirit or scope of the disclosure.
* * * * *